Paper Citation Record · LEDGER

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml

As of 9 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2602.15751.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2602.15751 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:38:13.957246Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

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External citation measurements

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Outbound references

Observation 9e4bee9f-4941-48f0-9999-b0747d845c09 · outbound

This paper cites Zurbano Fernandez et al.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Zurbano Fernandez et al

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:12.725579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09adc19a-be14-445f-9f5c-ca26ce8676af · outbound

This paper cites Radiation effects in the lhc experiments: Impact on detector performance and operation.CERN Yellow Reports: Monographs, Geneva: CERN, pages 87–122, 2021.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Radiation effects in the lhc experiments: Impact on detector performance and operation.CERN Yellow Reports: Monographs, Geneva: CERN, pages 87–122, 2021

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:12.853460Z

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Observation 41899b5c-2f84-4f08-9bad-3ee12e5bbee2 · outbound

This paper cites Physics case for an LHCb Upgrade II - Opportunities in flavour physics, and beyond, in the HL-LHC era.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Physics case for an LHCb Upgrade II - Opportunities in flavour physics, and beyond, in the HL-LHC era

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:12.973610Z

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Observation 81abb4f0-1340-470c-b4c2-84e6d52afe74 · outbound

This paper cites an unresolved cited work.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.111243Z

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Observation bfa973e8-a324-4e05-b034-34680a157ebd · outbound

This paper cites Spider, a waveform digitizer asic for picosecond timing in lhcb picocal, 2025.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Spider, a waveform digitizer asic for picosecond timing in lhcb picocal, 2025

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.209983Z

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Observation 8366f49a-de23-4aa2-adda-323817043ba4 · outbound

This paper cites Fast inference of deep neural networks in FPGAs for particle physics.JINST, 13(07):P07027, 2018.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Fast inference of deep neural networks in FPGAs for particle physics.JINST, 13(07):P07027, 2018

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.315912Z

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Observation 4763eb8e-7508-4b52-a33a-959fcada76bd · outbound

This paper cites hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.406043Z

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Observation 3cad76a7-945d-49ab-9491-0c5d5289dbdc · outbound

This paper cites Machine learning at the energy and intensity frontiers of particle physics.Nature, 560(7716):41–48, 2018.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Machine learning at the energy and intensity frontiers of particle physics.Nature, 560(7716):41–48, 2018

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.448764Z

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Observation db99bbc7-b715-4885-9dcb-d4c5ef613e42 · outbound

This paper cites Searching for new physics with deep autoencoders.Phys.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Searching for new physics with deep autoencoders.Phys

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.484493Z

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Observation 36fe9d93-c696-48d2-85c2-ebc2d438d7d4 · outbound

This paper cites Decoding photons: Physics in the latent space of a bib-ae generative network.EPJ Web Conf., 251:03003, 2021.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Decoding photons: Physics in the latent space of a bib-ae generative network.EPJ Web Conf., 251:03003, 2021

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.523060Z

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Observation 9e2ee419-c52f-4348-97fc-4d908c3041cc · outbound

This paper cites On the optimal design of triple modular redundancy logic for sram-based fpgas.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml On the optimal design of triple modular redundancy logic for sram-based fpgas

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.569614Z

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Observation e039d343-058b-4344-b99f-0dab8956633b · outbound

This paper cites Technical report, CERN, Geneva, 2021.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Technical report, CERN, Geneva, 2021

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.614841Z

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Observation 873bffd2-cc9c-4b0b-b202-3f72d1250ec4 · outbound

This paper cites Agostinelli et al.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Agostinelli et al

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.657721Z

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Observation 398e757a-796f-461c-aa19-3117dee03a45 · outbound

This paper cites The lhcb picocal.Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1079:170608, 2025.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml The lhcb picocal.Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1079:170608, 2025

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.712877Z

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Observation 3207283a-5bec-43fc-96e2-b3ac892541b2 · outbound

This paper cites TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml TensorFlow: Large-scale machine learning on heterogeneous systems, 2015

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.753919Z

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Observation b96b657f-ed94-465b-9feb-ef0feb5ec266 · outbound

This paper cites Keras.https://keras.io, 2015.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Keras.https://keras.io, 2015

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.805258Z

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Observation b1a93dd8-2915-4349-8de5-eae29fab333c · outbound

This paper cites Gedcke and W.J.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Gedcke and W.J

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.859928Z

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Observation 26c4d31e-5915-4337-914e-1c3e0971a77b · outbound

This paper cites Fkeras: A sensitivity analysis tool for edge neural networks.ACM J.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Fkeras: A sensitivity analysis tool for edge neural networks.ACM J

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.913026Z

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Observation 00213100-0cea-43b8-bf42-6918b3cdcc42 · outbound

This paper cites Faq: Mitigating the impact of faults in the weight memory of dnn accelerators through fault-aware quantization.

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml Faq: Mitigating the impact of faults in the weight memory of dnn accelerators through fault-aware quantization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T02:38:13.957246Z

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Pith citing papers

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